{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "22b06768",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import date, timedelta\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "linelist_deaths = 'https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/linelist/linelist_deaths.csv'\n",
    "vax_agg = 'https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/vaccination/vax_malaysia.csv'\n",
    "\n",
    "date_min = date(2021,5,24) # Phase 2 vax started 19 Apr; 2nd dose on 10 May; fully vax on 24 May\n",
    "date_max = date.today() - timedelta(8) # most recent 7 days incomplete\n",
    "data_range = 'data from ' + date_min.strftime('%d-%b') + ' to ' + date_max.strftime('%d-%b')\n",
    "total_adults = 23966637 # same as other notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "35140c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>status</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1188</th>\n",
       "      <td>2022-06-24</td>\n",
       "      <td>boosted</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1189</th>\n",
       "      <td>2022-06-24</td>\n",
       "      <td>fullyvax</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1190</th>\n",
       "      <td>2022-06-24</td>\n",
       "      <td>unvax</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1191</th>\n",
       "      <td>2022-06-25</td>\n",
       "      <td>boosted</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>2022-06-25</td>\n",
       "      <td>fullyvax</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>2022-06-25</td>\n",
       "      <td>unvax</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>2022-06-26</td>\n",
       "      <td>boosted</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>2022-06-26</td>\n",
       "      <td>fullyvax</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>2022-06-26</td>\n",
       "      <td>unvax</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>2022-06-27</td>\n",
       "      <td>boosted</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>2022-06-27</td>\n",
       "      <td>fullyvax</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>2022-06-27</td>\n",
       "      <td>unvax</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>2022-06-28</td>\n",
       "      <td>boosted</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>2022-06-28</td>\n",
       "      <td>fullyvax</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>2022-06-28</td>\n",
       "      <td>unvax</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>2022-06-29</td>\n",
       "      <td>boosted</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>2022-06-29</td>\n",
       "      <td>fullyvax</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>2022-06-29</td>\n",
       "      <td>unvax</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date    status  deaths\n",
       "1188  2022-06-24   boosted       1\n",
       "1189  2022-06-24  fullyvax       1\n",
       "1190  2022-06-24     unvax       2\n",
       "1191  2022-06-25   boosted       0\n",
       "1192  2022-06-25  fullyvax       3\n",
       "1193  2022-06-25     unvax       1\n",
       "1194  2022-06-26   boosted       1\n",
       "1195  2022-06-26  fullyvax       1\n",
       "1196  2022-06-26     unvax       3\n",
       "1197  2022-06-27   boosted       3\n",
       "1198  2022-06-27  fullyvax       1\n",
       "1199  2022-06-27     unvax       1\n",
       "1200  2022-06-28   boosted       2\n",
       "1201  2022-06-28  fullyvax       1\n",
       "1202  2022-06-28     unvax       0\n",
       "1203  2022-06-29   boosted       2\n",
       "1204  2022-06-29  fullyvax       1\n",
       "1205  2022-06-29     unvax       0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def vaxStatus(date_pos, date1, date2, date3):\n",
    "    if (date_pos - date3).days > 6: return 'boosted'\n",
    "    elif (date_pos - date2).days > 13: return 'fullyvax'\n",
    "    elif (date_pos - date1).days >= 0: return 'partialvax'\n",
    "    else: return 'unvax'\n",
    "    \n",
    "\n",
    "# Pull latest deaths linelist and wrangle\n",
    "cols_date = ['date', 'date_positive', 'date_dose1', 'date_dose2', 'date_dose3']\n",
    "df = pd.read_csv(linelist_deaths,\n",
    "                 usecols=cols_date + ['brand1','age'])\n",
    "for c in cols_date: df[c] = pd.to_datetime(df[c],errors='coerce').dt.date\n",
    "df = df[(df.date >= date_min) & (df.date <= date_max)]\n",
    "df = df[df.age > 17] # adults only\n",
    "df.drop(['age'],axis=1,inplace=True)\n",
    "\n",
    "# Ensure no null vax dates (future date as placeholder), shift 14 days for Cansino, then encode vax status\n",
    "for c in ['date_dose1', 'date_dose2', 'date_dose3']: df[c] = df[c].fillna(date.today() + timedelta(1))\n",
    "df.loc[df.brand1.isin(['Cansino']), 'date_dose2'] = df.date_dose1 + timedelta(14)\n",
    "df['status'] = df.apply(lambda x: vaxStatus(x['date_positive'], x['date_dose1'], x['date_dose2'], x['date_dose3']), axis=1)\n",
    "df = df.replace(date.today() + timedelta(1), np.nan)  # Remove placeholder dates\n",
    "df['deaths'] = 1\n",
    "\n",
    "df.date = pd.to_datetime(df.date)\n",
    "df = df.groupby(['date', 'status']).sum() \\\n",
    "                .unstack(fill_value=0) \\\n",
    "                .asfreq('D',fill_value=0) \\\n",
    "                .stack() \\\n",
    "                .reset_index() # Typically, unstack/stack suffices, but this is robust to having dates with no deaths\n",
    "df.date = df.date.dt.date\n",
    "df = df[~df.status.isin(['partialvax'])].reset_index(drop=True)\n",
    "df[-18:].head(18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00d778d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>status</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1188</th>\n",
       "      <td>2022-06-24</td>\n",
       "      <td>boosted</td>\n",
       "      <td>16130973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1189</th>\n",
       "      <td>2022-06-24</td>\n",
       "      <td>fullyvax</td>\n",
       "      <td>6892422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1190</th>\n",
       "      <td>2022-06-24</td>\n",
       "      <td>unvax</td>\n",
       "      <td>663735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1191</th>\n",
       "      <td>2022-06-25</td>\n",
       "      <td>boosted</td>\n",
       "      <td>16131799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>2022-06-25</td>\n",
       "      <td>fullyvax</td>\n",
       "      <td>6893558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>2022-06-25</td>\n",
       "      <td>unvax</td>\n",
       "      <td>663632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>2022-06-26</td>\n",
       "      <td>boosted</td>\n",
       "      <td>16132336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1195</th>\n",
       "      <td>2022-06-26</td>\n",
       "      <td>fullyvax</td>\n",
       "      <td>6893128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>2022-06-26</td>\n",
       "      <td>unvax</td>\n",
       "      <td>663578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>2022-06-27</td>\n",
       "      <td>boosted</td>\n",
       "      <td>16133596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>2022-06-27</td>\n",
       "      <td>fullyvax</td>\n",
       "      <td>6892074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199</th>\n",
       "      <td>2022-06-27</td>\n",
       "      <td>unvax</td>\n",
       "      <td>663501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200</th>\n",
       "      <td>2022-06-28</td>\n",
       "      <td>boosted</td>\n",
       "      <td>16134942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1201</th>\n",
       "      <td>2022-06-28</td>\n",
       "      <td>fullyvax</td>\n",
       "      <td>6890663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1202</th>\n",
       "      <td>2022-06-28</td>\n",
       "      <td>unvax</td>\n",
       "      <td>663423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203</th>\n",
       "      <td>2022-06-29</td>\n",
       "      <td>boosted</td>\n",
       "      <td>16136295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1204</th>\n",
       "      <td>2022-06-29</td>\n",
       "      <td>fullyvax</td>\n",
       "      <td>6889582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1205</th>\n",
       "      <td>2022-06-29</td>\n",
       "      <td>unvax</td>\n",
       "      <td>663261</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date    status  population\n",
       "1188  2022-06-24   boosted    16130973\n",
       "1189  2022-06-24  fullyvax     6892422\n",
       "1190  2022-06-24     unvax      663735\n",
       "1191  2022-06-25   boosted    16131799\n",
       "1192  2022-06-25  fullyvax     6893558\n",
       "1193  2022-06-25     unvax      663632\n",
       "1194  2022-06-26   boosted    16132336\n",
       "1195  2022-06-26  fullyvax     6893128\n",
       "1196  2022-06-26     unvax      663578\n",
       "1197  2022-06-27   boosted    16133596\n",
       "1198  2022-06-27  fullyvax     6892074\n",
       "1199  2022-06-27     unvax      663501\n",
       "1200  2022-06-28   boosted    16134942\n",
       "1201  2022-06-28  fullyvax     6890663\n",
       "1202  2022-06-28     unvax      663423\n",
       "1203  2022-06-29   boosted    16136295\n",
       "1204  2022-06-29  fullyvax     6889582\n",
       "1205  2022-06-29     unvax      663261"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shifts = {'cumul_partial_adult': 1, 'cumul_full_adult': 14, 'cumul_booster':7}\n",
    "\n",
    "vf = pd.read_csv(vax_agg)\n",
    "vf.date = pd.to_datetime(vf.date).dt.date\n",
    "vf['cumul_partial_adult'] = vf.cumul_partial - vf.cumul_partial_adol - vf.cumul_partial_child\n",
    "vf['unvax_adult'] = total_adults - vf.cumul_partial_adult\n",
    "vf['cumul_full_adult'] = vf.cumul_full - vf.cumul_full_adol - vf.cumul_full_child - vf.cumul_booster\n",
    "vf['cumul_partial_adult'] = vf['cumul_partial_adult'] - vf['cumul_full_adult'] - vf.cumul_booster\n",
    "\n",
    "for c in ['cumul_partial_adult', 'cumul_full_adult','cumul_booster']: vf[c] = vf[c].shift(shifts[c]).fillna(0).astype(int)\n",
    "vf = vf[['date','unvax_adult','cumul_partial_adult','cumul_full_adult','cumul_booster']]\n",
    "col_status = ['unvax','partialvax','fullyvax','boosted']\n",
    "vf.columns = ['date'] + col_status\n",
    "vf = pd.melt(vf, id_vars=['date'], value_vars=col_status)\n",
    "vf.columns = ['date','status','population']\n",
    "vf = vf[(vf.date >= date_min) & (vf.date <= date_max)]\n",
    "vf = vf[~vf.status.isin(['partialvax'])].sort_values(by=['date','status']).reset_index(drop=True)\n",
    "vf[-18:].head(18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34bd49f1",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Unvaccinated</th>\n",
       "      <th>Fully Vaccinated</th>\n",
       "      <th>Boosted</th>\n",
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       "      <th>388</th>\n",
       "      <td>2022-06-16</td>\n",
       "      <td>0.085736</td>\n",
       "      <td>0.012414</td>\n",
       "      <td>0.007978</td>\n",
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       "      <th>389</th>\n",
       "      <td>2022-06-17</td>\n",
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       "      <th>390</th>\n",
       "      <td>2022-06-18</td>\n",
       "      <td>0.042913</td>\n",
       "      <td>0.008278</td>\n",
       "      <td>0.005319</td>\n",
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       "      <th>391</th>\n",
       "      <td>2022-06-19</td>\n",
       "      <td>0.085905</td>\n",
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       "      <td>2022-06-23</td>\n",
       "      <td>0.064495</td>\n",
       "      <td>0.006213</td>\n",
       "      <td>0.005315</td>\n",
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       "      <td>2022-06-24</td>\n",
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       "      <td>0.006201</td>\n",
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       "      <td>0.005314</td>\n",
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       "      <td>2022-06-27</td>\n",
       "      <td>0.172193</td>\n",
       "      <td>0.014506</td>\n",
       "      <td>0.007085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>2022-06-28</td>\n",
       "      <td>0.150689</td>\n",
       "      <td>0.014508</td>\n",
       "      <td>0.007970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>2022-06-29</td>\n",
       "      <td>0.150689</td>\n",
       "      <td>0.016582</td>\n",
       "      <td>0.008855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  Unvaccinated  Fully Vaccinated   Boosted\n",
       "388  2022-06-16      0.085736          0.012414  0.007978\n",
       "389  2022-06-17      0.085736          0.008278  0.007092\n",
       "390  2022-06-18      0.042913          0.008278  0.005319\n",
       "391  2022-06-19      0.085905          0.010349  0.007091\n",
       "392  2022-06-20      0.064470          0.008282  0.005317\n",
       "393  2022-06-21      0.085974          0.010353  0.003544\n",
       "394  2022-06-22      0.064495          0.010353  0.004430\n",
       "395  2022-06-23      0.064495          0.006213  0.005315\n",
       "396  2022-06-24      0.107542          0.008286  0.006201\n",
       "397  2022-06-25      0.129068          0.014503  0.006201\n",
       "398  2022-06-26      0.150662          0.014504  0.005314\n",
       "399  2022-06-27      0.172193          0.014506  0.007085\n",
       "400  2022-06-28      0.150689          0.014508  0.007970\n",
       "401  2022-06-29      0.150689          0.016582  0.008855"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge frames and compute incidence, then get 7d rolling average\n",
    "df = pd.merge(df,vf, on=['date','status'], how='left')\n",
    "df['capita'] = df.deaths/df.population * 1e5\n",
    "df = df.pivot(index='date', columns='status', values=['capita']).fillna(0).reset_index()\n",
    "df.columns = ['date','Boosted','Fully Vaccinated','Unvaccinated']\n",
    "df = df[['date','Unvaccinated','Fully Vaccinated','Boosted']].set_index('date')\n",
    "df = df.rolling(7).mean().reset_index()\n",
    "df[-14:].head(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "54685078",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Chart\n",
    "plt.rcParams.update({'font.size': 11, 'font.family':'Monospace', 'grid.linestyle':'dashed'})\n",
    "plt.rcParams[\"figure.figsize\"] = [10,8]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "df.plot(x='date', y=['Unvaccinated','Fully Vaccinated','Boosted'], color=['#a70000','#bdbdbd','#183f78'], ax=ax)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "ax.yaxis.grid(True)\n",
    "ax.set_axisbelow(True)\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%d-%b'))\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(0.15, 0.95), ncol=1, labelspacing = 1.5, frameon=True, fancybox=True)\n",
    "\n",
    "plt.title('Deaths per 100k People by Vax Status \\n\\n (' + data_range + ')\\n')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
